Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This specific course emphasizes the integration of machine learning and AI with big data administration, utilizing Google Cloud services.
Description for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Managed Big Data Services: Investigate cloud-based services for the collection, storage, and management of large amounts of data, with a focus on scalability and efficiency.
Machine Learning Fundamentals: Offers a comprehensive comprehension of the potential applications of machine learning by introducing its value proposition and terminology.
Data Analysis Framework: Describes the effective organization and analysis of data in the cloud through the use of contemporary tools and methodologies.
Google Cloud AI Tools: Exhibits the practical application and configuration of machine learning and AI tools that are available on Google Cloud for advanced data processing.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On edX provided by GoogleCloud
Duration: 4�5 hours per week approx 1 week
Schedule: Flexible
Pricing for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Use Cases for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
FAQs for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Reviews for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
The AI tool specializes in sentiment analysis, competitive analysis, custom analytics, Amazon marketplace analysis, review export, comprehensive help resources, and social media presence to meet diverse user needs effectively.
The AI tool enables organizations to create personalized multi-channel experiences for their clientele, featuring audience segmentation and a user-friendly platform with a complimentary 14-day trial and enterprise pricing options.
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
The AI tool offers a wide range of features for marketing teams, including multi-platform support and team collaboration functionalities, facilitating efficient management and coordination of marketing efforts.
Careerdekho AI assists users in discovering suitable careers through personalized recommendations across diverse fields, offering a free AI assessment and expert consultations for refined career planning.
The AI-powered decision support tool offers predictive analytics, data visualization, and seamless integration, aiding users in making informed decisions efficiently, though it may require some time to master its advanced features.
The AI tool provides comprehensive support for task management in Scrum and Kanban, offering efficient planning tools and multi-language support, although it may have a learning curve and limited integrations.
Utilizing AI technology, this tool streamlines statistical analysis tasks, automates calculations, supports various data formats, and provides visualization tools for efficient and precise scientific research.
The AI generator, drawing from various sources, facilitates user interaction to produce content, making it beneficial for startups and individuals seeking to explore and enhance their knowledge across different subjects.
The tool employs AI to assist users in understanding intricate documents, offering features such as content analysis, summarization, and language analysis, with plans for further enhancements.
Featured Tools
Master the implementation of deep learning algorithms using PyTorch, covering Deep Neural Networks and machine learning techniques, along with Python library utilization, to construct and deploy deep neural networks effectively.
Explore healthcare data mining methods, theoretical foundations of key techniques, selection criteria, and practical applications with emphasis on data cleansing, transformation, and modeling for real-world problem solving.
The course covers the fundamentals of unsupervised learning methods and their real-world applications, particularly recommender systems.
Understand the core concepts of data analytics, its primary phases, key data roles, various data structures, file formats, and the comprehensive data analysis process.
Understand Python methodologies like lambdas, csv file manipulation, and prevalent data science features, including cleansing and processing DataFrame structures.